{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "BTP MACHINE HACK.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uRUGa29YOROI"
      },
      "source": [
        "Submission By : Deep Contractor\r\n",
        "\r\n",
        "Email : deep.contractor88@gmail.com\r\n",
        "\r\n",
        "Linkedin : https://www.linkedin.com/in/deep-contractor-85b508168/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJXwgaiCWRBx"
      },
      "source": [
        "# IMPORT LIBRARIES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "blDwyxKmYS6X"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns\r\n",
        "import random\r\n",
        "import datetime\r\n",
        "%matplotlib inline"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uNqjjsd9WWQA"
      },
      "source": [
        "# IMPORTING DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5aoA97f5Yitd"
      },
      "source": [
        "testdata = pd.read_csv('/content/drive/MyDrive/Data/ParticipantData_BTPC/Test.csv')\r\n",
        "traindata = pd.read_csv('/content/drive/MyDrive/Data/ParticipantData_BTPC/Train.csv')\r\n",
        "sampledata = ('/content/drive/MyDrive/Data/ParticipantData_BTPC/Sample Submission.csv')"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "vbxLpy-gYmIH",
        "outputId": "cad4b016-8b3a-4ae9-f29e-ff802b5b9910"
      },
      "source": [
        "traindata.head()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>session_id</th>\n",
              "      <th>session_number</th>\n",
              "      <th>client_agent</th>\n",
              "      <th>device_details</th>\n",
              "      <th>date</th>\n",
              "      <th>purchased</th>\n",
              "      <th>added_in_cart</th>\n",
              "      <th>checked_out</th>\n",
              "      <th>time_spent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>57f879e70d3c5fc2a98102d64c9fd84e</td>\n",
              "      <td>715</td>\n",
              "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
              "      <td>Desktop - Chrome</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>236.886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a5442b0c7c33d0a811e7661e556b2de8</td>\n",
              "      <td>55</td>\n",
              "      <td>Product/8.0 iPhone/8.1.3</td>\n",
              "      <td>iPhone - iOS</td>\n",
              "      <td>2020-02-27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1277.455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>305cb1486ed8610c00b37007926cb2c4</td>\n",
              "      <td>11</td>\n",
              "      <td>Mozilla/5.0 (iPhone; CPU iPhone OS 7_1_2 like ...</td>\n",
              "      <td>iPhone - MobileWeb</td>\n",
              "      <td>2019-08-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21.704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>f2c1ecc9993f0071df91ba178450498c</td>\n",
              "      <td>2794</td>\n",
              "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
              "      <td>Desktop - IE</td>\n",
              "      <td>2019-12-30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>455.201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>e460830ae295e55d2216ebdc761ab9a6</td>\n",
              "      <td>3674</td>\n",
              "      <td>Mozilla/5.0 (iPhone; CPU iPhone OS 7_1_1 like ...</td>\n",
              "      <td>iPhone - Web</td>\n",
              "      <td>2019-09-10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1164.877</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         session_id  session_number  ... checked_out time_spent\n",
              "0  57f879e70d3c5fc2a98102d64c9fd84e             715  ...           0    236.886\n",
              "1  a5442b0c7c33d0a811e7661e556b2de8              55  ...           0   1277.455\n",
              "2  305cb1486ed8610c00b37007926cb2c4              11  ...           0     21.704\n",
              "3  f2c1ecc9993f0071df91ba178450498c            2794  ...           0    455.201\n",
              "4  e460830ae295e55d2216ebdc761ab9a6            3674  ...           0   1164.877\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YOpKnWOEYppY",
        "outputId": "a16a6020-ac9f-4b40-a8f1-48d69879fd23"
      },
      "source": [
        "mergedf = traindata.append(testdata)\r\n",
        "mergedf = mergedf.reset_index(drop=True)\r\n",
        "mergedf = mergedf.fillna('0')\r\n",
        "mergedf.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7756, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yuimzWxGWaEa"
      },
      "source": [
        "# FEATURE ENGINEERING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnuqFQQAY7Om"
      },
      "source": [
        "def device_used(df):\r\n",
        "    mergedf['device_used'] = np.nan\r\n",
        "    for i in range(len(mergedf)):\r\n",
        "        check = mergedf.loc[i,'device_details']\r\n",
        "        if('Tablet' in check):\r\n",
        "            mergedf.loc[i,'device_used'] = 'Tablet'\r\n",
        "        elif('iPad' in check):\r\n",
        "            mergedf.loc[i,'device_used'] = 'Tablet'\r\n",
        "        elif('Desktop' in check):\r\n",
        "            mergedf.loc[i,'device_used'] = 'Desktop'\r\n",
        "        elif('iPhone' in check):\r\n",
        "            mergedf.loc[i,'device_used'] = 'Smartphone'\r\n",
        "        elif('Android' in check):\r\n",
        "            mergedf.loc[i,'device_used'] = 'Smartphone'\r\n",
        "        elif('Unknown' in check):\r\n",
        "            mergedf.loc[i,'device_used'] = 'Smartphone'\r\n",
        "        elif('Other' in check):\r\n",
        "            mergedf.loc[i,'device_used'] = 'Others'\r\n",
        "    return df\r\n",
        "\r\n",
        "def medium_used(df):\r\n",
        "    mergedf['medium_used'] = np.nan\r\n",
        "    for i in range(len(mergedf)):\r\n",
        "        check = mergedf.loc[i,'device_details']\r\n",
        "        if('Desktop - Chrome' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'Chrome'\r\n",
        "        elif('iPhone - iOS' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'Application'\r\n",
        "        elif('iPhone - MobileWeb' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'MobileWeb'\r\n",
        "        elif('Desktop - IE' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'IE'\r\n",
        "        elif('iPhone - Web' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'Safari'\r\n",
        "        elif('Android Phone - Android' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'Application'\r\n",
        "        elif('Desktop - Firefox' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'Firefox'\r\n",
        "        elif('Android Tablet - Web' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'MobileWeb'\r\n",
        "        elif('iPad - Web' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'MobileWeb'\r\n",
        "        elif('Other - Other' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'Others'\r\n",
        "        elif('Desktop - Safari' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'Safari'\r\n",
        "        elif('Android Phone - Web' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'MobileWeb'\r\n",
        "        elif('iPad - iOS' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'Application'\r\n",
        "        elif('Android Phone - MobileWeb' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'MobileWeb'\r\n",
        "        elif('Unknown - MobileWeb' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'MobileWeb'\r\n",
        "        elif('Android Tablet - Android' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'Application'\r\n",
        "        elif('Android Tablet - MobileWeb' == check):\r\n",
        "            mergedf.loc[i,'medium_used'] = 'MobileWeb'\r\n",
        "    return df\r\n",
        "\r\n",
        "def os_used(df):\r\n",
        "    mergedf['os_used'] = np.nan\r\n",
        "    for i in range(len(mergedf)):\r\n",
        "        check = mergedf.loc[i,'device_details']\r\n",
        "        if('Desktop - Chrome' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'Windows'\r\n",
        "        elif('iPhone - iOS' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'ios'\r\n",
        "        elif('iPhone - MobileWeb' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'ios'\r\n",
        "        elif('Desktop - IE' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'Windows'\r\n",
        "        elif('iPhone - Web' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'ios'\r\n",
        "        elif('Android Phone - Android' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'Android'\r\n",
        "        elif('Desktop - Firefox' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'Windows'\r\n",
        "        elif('Android Tablet - Web' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'Android'\r\n",
        "        elif('iPad - Web' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'ios'\r\n",
        "        elif('Other - Other' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'Others'\r\n",
        "        elif('Desktop - Safari' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'MacOS'\r\n",
        "        elif('Android Phone - Web' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'Android'\r\n",
        "        elif('iPad - iOS' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'ios'\r\n",
        "        elif('Android Phone - MobileWeb' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'Android'\r\n",
        "        elif('Unknown - MobileWeb' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'Unknown'\r\n",
        "        elif('Android Tablet - Android' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'Android'\r\n",
        "        elif('Android Tablet - MobileWeb' == check):\r\n",
        "            mergedf.loc[i,'os_used'] = 'Android'\r\n",
        "    return df"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7C9TEotHdNHP"
      },
      "source": [
        "mergedf = medium_used(mergedf)\r\n",
        "mergedf = device_used(mergedf)\r\n",
        "mergedf = os_used(mergedf)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C-BPr2k8zt_s",
        "outputId": "1fa31be0-1924-4e26-dd47-d0a453d6cc2a"
      },
      "source": [
        "mergedf.device_used.value_counts()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Smartphone    4260\n",
              "Desktop       2380\n",
              "Tablet         849\n",
              "Others         267\n",
              "Name: device_used, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rbgwFEVOzh1y",
        "outputId": "8b080a6d-d7c2-4293-da17-9f2d28edb063"
      },
      "source": [
        "mergedf.os_used.value_counts()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ios        4037\n",
              "Windows    1937\n",
              "Android     950\n",
              "MacOS       443\n",
              "Others      267\n",
              "Unknown     122\n",
              "Name: os_used, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XtZoPJA-vhgi",
        "outputId": "eedb9f3a-e85c-4b65-8e3d-2a4d97c07984"
      },
      "source": [
        "mergedf.medium_used.value_counts()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Application    2716\n",
              "MobileWeb      1449\n",
              "Safari         1387\n",
              "Chrome         1181\n",
              "IE              429\n",
              "Firefox         327\n",
              "Others          267\n",
              "Name: medium_used, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6SLn6Boz_px"
      },
      "source": [
        "mergedf = mergedf.drop(['session_id','client_agent','device_details'],axis=1)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "SXrotITP0F94",
        "outputId": "95eeae41-cbf9-4840-d237-e0f206a2d47d"
      },
      "source": [
        "mergedf.head()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>session_number</th>\n",
              "      <th>date</th>\n",
              "      <th>purchased</th>\n",
              "      <th>added_in_cart</th>\n",
              "      <th>checked_out</th>\n",
              "      <th>time_spent</th>\n",
              "      <th>medium_used</th>\n",
              "      <th>device_used</th>\n",
              "      <th>os_used</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>715</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>236.886</td>\n",
              "      <td>Chrome</td>\n",
              "      <td>Desktop</td>\n",
              "      <td>Windows</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55</td>\n",
              "      <td>2020-02-27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1277.45</td>\n",
              "      <td>Application</td>\n",
              "      <td>Smartphone</td>\n",
              "      <td>ios</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11</td>\n",
              "      <td>2019-08-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21.704</td>\n",
              "      <td>MobileWeb</td>\n",
              "      <td>Smartphone</td>\n",
              "      <td>ios</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2794</td>\n",
              "      <td>2019-12-30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>455.201</td>\n",
              "      <td>IE</td>\n",
              "      <td>Desktop</td>\n",
              "      <td>Windows</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3674</td>\n",
              "      <td>2019-09-10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1164.88</td>\n",
              "      <td>Safari</td>\n",
              "      <td>Smartphone</td>\n",
              "      <td>ios</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   session_number        date  purchased  ...  medium_used  device_used  os_used\n",
              "0             715  2020-01-22          1  ...       Chrome      Desktop  Windows\n",
              "1              55  2020-02-27          1  ...  Application   Smartphone      ios\n",
              "2              11  2019-08-01          0  ...    MobileWeb   Smartphone      ios\n",
              "3            2794  2019-12-30          0  ...           IE      Desktop  Windows\n",
              "4            3674  2019-09-10          0  ...       Safari   Smartphone      ios\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zD4-ypV0cDT"
      },
      "source": [
        "def Datefix(df):\r\n",
        "    df['date']= pd.to_datetime(df['date']) \r\n",
        "    df['month'] = df['date'].dt.month\r\n",
        "    df['day'] = df['date'].dt.day\r\n",
        "    df['year'] = df['date'].dt.year\r\n",
        "    df['quarter'] = df['date'].dt.quarter\r\n",
        "    df['weekname'] = df['date'].dt.day_name()\r\n",
        "    df['is_weekend'] = np.where(df['weekname'].isin(['Sunday','Saturday']),1,0)\r\n",
        "    return df"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdsr1dsf4xwj"
      },
      "source": [
        "mergedf = Datefix(mergedf)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7LZinSzyM1vC",
        "outputId": "d75e4b34-81dd-4df6-dbd2-7429e86cee7d"
      },
      "source": [
        "mergedf.month.value_counts()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9     1186\n",
              "12    1100\n",
              "11     806\n",
              "1      800\n",
              "10     789\n",
              "2      744\n",
              "8      722\n",
              "7      587\n",
              "3      422\n",
              "6      337\n",
              "5      148\n",
              "4      115\n",
              "Name: month, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mX7hsrC-M1pr",
        "outputId": "4ffa3ddb-fe96-4246-fb1b-376725dc203f"
      },
      "source": [
        "mergedf.day.value_counts()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6     313\n",
              "8     311\n",
              "2     304\n",
              "9     289\n",
              "22    288\n",
              "28    284\n",
              "5     283\n",
              "23    282\n",
              "11    278\n",
              "15    274\n",
              "1     270\n",
              "7     261\n",
              "24    255\n",
              "10    251\n",
              "27    247\n",
              "29    246\n",
              "3     244\n",
              "13    242\n",
              "30    241\n",
              "4     239\n",
              "26    238\n",
              "19    238\n",
              "14    236\n",
              "17    235\n",
              "16    230\n",
              "12    222\n",
              "20    221\n",
              "25    210\n",
              "21    193\n",
              "18    193\n",
              "31    138\n",
              "Name: day, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4SgwzgjUM4e0",
        "outputId": "3bd299ad-ac86-4705-d76f-3afa786daf5c"
      },
      "source": [
        "mergedf.year.value_counts()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2019    5675\n",
              "2020    2081\n",
              "Name: year, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cV6Zlv1YM4b7",
        "outputId": "9dd3a994-0b37-427a-91f3-ed667c11e520"
      },
      "source": [
        "mergedf.quarter.value_counts()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    2695\n",
              "3    2495\n",
              "1    1966\n",
              "2     600\n",
              "Name: quarter, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hEs4CqUwM_wi",
        "outputId": "653f7f29-ce27-4a72-fd41-596c586ed61d"
      },
      "source": [
        "mergedf.weekname.value_counts()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sunday       1284\n",
              "Monday       1232\n",
              "Tuesday      1209\n",
              "Wednesday    1132\n",
              "Thursday     1014\n",
              "Friday        962\n",
              "Saturday      923\n",
              "Name: weekname, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1aA7fSOG6jcB"
      },
      "source": [
        "from sklearn import preprocessing  \r\n",
        "label_encoder = preprocessing.LabelEncoder()  \r\n",
        "mergedf['device_used'] = label_encoder.fit_transform(mergedf['device_used'])\r\n",
        "mergedf['medium_used'] = label_encoder.fit_transform(mergedf['medium_used'])  \r\n",
        "mergedf['os_used'] = label_encoder.fit_transform(mergedf['os_used'])\r\n",
        "mergedf['weekname'] = label_encoder.fit_transform(mergedf['weekname'])\r\n",
        "mergedf['year'] = label_encoder.fit_transform(mergedf['year'])"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_O4b7RC1EmO"
      },
      "source": [
        "train_data = mergedf[:5429]\r\n",
        "test_data = mergedf[5429:]\r\n",
        "test_data = test_data.reset_index(drop=True)\r\n",
        "test_data = test_data.drop(['time_spent','date'],axis = 1)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1wTfPDH7DZJ",
        "outputId": "b25bfc43-0c2c-4998-b278-80137b7d7832"
      },
      "source": [
        "train_data['time_spent'] = train_data['time_spent'].astype(np.float64)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "VzEXK3Zp7Vj4",
        "outputId": "a2616db5-ea2d-4880-bf38-b3ad55fee526"
      },
      "source": [
        "train_data.corr()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>session_number</th>\n",
              "      <th>purchased</th>\n",
              "      <th>added_in_cart</th>\n",
              "      <th>checked_out</th>\n",
              "      <th>time_spent</th>\n",
              "      <th>medium_used</th>\n",
              "      <th>device_used</th>\n",
              "      <th>os_used</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>year</th>\n",
              "      <th>quarter</th>\n",
              "      <th>weekname</th>\n",
              "      <th>is_weekend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>session_number</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.213733</td>\n",
              "      <td>0.003013</td>\n",
              "      <td>-0.054500</td>\n",
              "      <td>0.078437</td>\n",
              "      <td>0.037864</td>\n",
              "      <td>0.206771</td>\n",
              "      <td>0.173426</td>\n",
              "      <td>0.076441</td>\n",
              "      <td>-0.002180</td>\n",
              "      <td>0.010174</td>\n",
              "      <td>0.035236</td>\n",
              "      <td>-0.004978</td>\n",
              "      <td>-0.006856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purchased</th>\n",
              "      <td>-0.213733</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.045022</td>\n",
              "      <td>0.168513</td>\n",
              "      <td>0.200762</td>\n",
              "      <td>-0.077132</td>\n",
              "      <td>-0.204488</td>\n",
              "      <td>-0.052645</td>\n",
              "      <td>-0.052517</td>\n",
              "      <td>-0.011841</td>\n",
              "      <td>0.058483</td>\n",
              "      <td>-0.042185</td>\n",
              "      <td>0.005174</td>\n",
              "      <td>0.003793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>added_in_cart</th>\n",
              "      <td>0.003013</td>\n",
              "      <td>0.045022</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.215754</td>\n",
              "      <td>0.209807</td>\n",
              "      <td>-0.115092</td>\n",
              "      <td>-0.104202</td>\n",
              "      <td>-0.077701</td>\n",
              "      <td>-0.032181</td>\n",
              "      <td>0.018657</td>\n",
              "      <td>0.032900</td>\n",
              "      <td>-0.032628</td>\n",
              "      <td>0.020016</td>\n",
              "      <td>-0.041200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>checked_out</th>\n",
              "      <td>-0.054500</td>\n",
              "      <td>0.168513</td>\n",
              "      <td>0.215754</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.157765</td>\n",
              "      <td>-0.025088</td>\n",
              "      <td>-0.097741</td>\n",
              "      <td>-0.007433</td>\n",
              "      <td>-0.010186</td>\n",
              "      <td>-0.003823</td>\n",
              "      <td>0.008054</td>\n",
              "      <td>-0.011371</td>\n",
              "      <td>0.023959</td>\n",
              "      <td>0.003444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time_spent</th>\n",
              "      <td>0.078437</td>\n",
              "      <td>0.200762</td>\n",
              "      <td>0.209807</td>\n",
              "      <td>0.157765</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.059106</td>\n",
              "      <td>-0.027572</td>\n",
              "      <td>0.006396</td>\n",
              "      <td>0.013367</td>\n",
              "      <td>-0.030206</td>\n",
              "      <td>-0.030843</td>\n",
              "      <td>0.001644</td>\n",
              "      <td>-0.016746</td>\n",
              "      <td>-0.003068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>medium_used</th>\n",
              "      <td>0.037864</td>\n",
              "      <td>-0.077132</td>\n",
              "      <td>-0.115092</td>\n",
              "      <td>-0.025088</td>\n",
              "      <td>-0.059106</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005952</td>\n",
              "      <td>-0.179644</td>\n",
              "      <td>0.068453</td>\n",
              "      <td>0.006510</td>\n",
              "      <td>-0.086983</td>\n",
              "      <td>0.061276</td>\n",
              "      <td>-0.024090</td>\n",
              "      <td>0.027875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>device_used</th>\n",
              "      <td>0.206771</td>\n",
              "      <td>-0.204488</td>\n",
              "      <td>-0.104202</td>\n",
              "      <td>-0.097741</td>\n",
              "      <td>-0.027572</td>\n",
              "      <td>-0.005952</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.106887</td>\n",
              "      <td>0.041215</td>\n",
              "      <td>-0.009485</td>\n",
              "      <td>-0.038686</td>\n",
              "      <td>0.024788</td>\n",
              "      <td>-0.043043</td>\n",
              "      <td>0.003435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>os_used</th>\n",
              "      <td>0.173426</td>\n",
              "      <td>-0.052645</td>\n",
              "      <td>-0.077701</td>\n",
              "      <td>-0.007433</td>\n",
              "      <td>0.006396</td>\n",
              "      <td>-0.179644</td>\n",
              "      <td>0.106887</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.083042</td>\n",
              "      <td>0.011012</td>\n",
              "      <td>-0.042364</td>\n",
              "      <td>0.082660</td>\n",
              "      <td>-0.029207</td>\n",
              "      <td>0.017785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>0.076441</td>\n",
              "      <td>-0.052517</td>\n",
              "      <td>-0.032181</td>\n",
              "      <td>-0.010186</td>\n",
              "      <td>0.013367</td>\n",
              "      <td>0.068453</td>\n",
              "      <td>0.041215</td>\n",
              "      <td>0.083042</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.050067</td>\n",
              "      <td>-0.884790</td>\n",
              "      <td>0.975688</td>\n",
              "      <td>-0.035060</td>\n",
              "      <td>0.016119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <td>-0.002180</td>\n",
              "      <td>-0.011841</td>\n",
              "      <td>0.018657</td>\n",
              "      <td>-0.003823</td>\n",
              "      <td>-0.030206</td>\n",
              "      <td>0.006510</td>\n",
              "      <td>-0.009485</td>\n",
              "      <td>0.011012</td>\n",
              "      <td>0.050067</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.087050</td>\n",
              "      <td>0.069822</td>\n",
              "      <td>-0.020627</td>\n",
              "      <td>-0.025156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0.010174</td>\n",
              "      <td>0.058483</td>\n",
              "      <td>0.032900</td>\n",
              "      <td>0.008054</td>\n",
              "      <td>-0.030843</td>\n",
              "      <td>-0.086983</td>\n",
              "      <td>-0.038686</td>\n",
              "      <td>-0.042364</td>\n",
              "      <td>-0.884790</td>\n",
              "      <td>-0.087050</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.880020</td>\n",
              "      <td>0.024067</td>\n",
              "      <td>-0.005468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quarter</th>\n",
              "      <td>0.035236</td>\n",
              "      <td>-0.042185</td>\n",
              "      <td>-0.032628</td>\n",
              "      <td>-0.011371</td>\n",
              "      <td>0.001644</td>\n",
              "      <td>0.061276</td>\n",
              "      <td>0.024788</td>\n",
              "      <td>0.082660</td>\n",
              "      <td>0.975688</td>\n",
              "      <td>0.069822</td>\n",
              "      <td>-0.880020</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.026054</td>\n",
              "      <td>0.008511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weekname</th>\n",
              "      <td>-0.004978</td>\n",
              "      <td>0.005174</td>\n",
              "      <td>0.020016</td>\n",
              "      <td>0.023959</td>\n",
              "      <td>-0.016746</td>\n",
              "      <td>-0.024090</td>\n",
              "      <td>-0.043043</td>\n",
              "      <td>-0.029207</td>\n",
              "      <td>-0.035060</td>\n",
              "      <td>-0.020627</td>\n",
              "      <td>0.024067</td>\n",
              "      <td>-0.026054</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.154825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_weekend</th>\n",
              "      <td>-0.006856</td>\n",
              "      <td>0.003793</td>\n",
              "      <td>-0.041200</td>\n",
              "      <td>0.003444</td>\n",
              "      <td>-0.003068</td>\n",
              "      <td>0.027875</td>\n",
              "      <td>0.003435</td>\n",
              "      <td>0.017785</td>\n",
              "      <td>0.016119</td>\n",
              "      <td>-0.025156</td>\n",
              "      <td>-0.005468</td>\n",
              "      <td>0.008511</td>\n",
              "      <td>-0.154825</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                session_number  purchased  ...  weekname  is_weekend\n",
              "session_number        1.000000  -0.213733  ... -0.004978   -0.006856\n",
              "purchased            -0.213733   1.000000  ...  0.005174    0.003793\n",
              "added_in_cart         0.003013   0.045022  ...  0.020016   -0.041200\n",
              "checked_out          -0.054500   0.168513  ...  0.023959    0.003444\n",
              "time_spent            0.078437   0.200762  ... -0.016746   -0.003068\n",
              "medium_used           0.037864  -0.077132  ... -0.024090    0.027875\n",
              "device_used           0.206771  -0.204488  ... -0.043043    0.003435\n",
              "os_used               0.173426  -0.052645  ... -0.029207    0.017785\n",
              "month                 0.076441  -0.052517  ... -0.035060    0.016119\n",
              "day                  -0.002180  -0.011841  ... -0.020627   -0.025156\n",
              "year                  0.010174   0.058483  ...  0.024067   -0.005468\n",
              "quarter               0.035236  -0.042185  ... -0.026054    0.008511\n",
              "weekname             -0.004978   0.005174  ...  1.000000   -0.154825\n",
              "is_weekend           -0.006856   0.003793  ... -0.154825    1.000000\n",
              "\n",
              "[14 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "qQ6gtRJaNLRw",
        "outputId": "84989829-ff6e-46f0-ea4a-371b43d910c5"
      },
      "source": [
        "sns.heatmap(data = train_data.corr())"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a56ba5390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "JeIur-CU9COt",
        "outputId": "e9e2c638-839c-4114-ce87-52c9baf23539"
      },
      "source": [
        "train_data.hist(bins=50, figsize=(15, 15))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55df1278>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55d986a0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55d46908>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55d77b70>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55d29dd8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55ce6080>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55c962e8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55c47518>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55c47588>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55c2da20>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55be1c88>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55b96ef0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55b4cd30>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55b0c0f0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55b3c470>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7a55aed7f0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQr_JKhbK-E7"
      },
      "source": [
        "X = train_data.drop(['time_spent','date','quarter','month'],axis=1)\r\n",
        "Y = train_data['time_spent']\r\n",
        "Y=Y.to_frame()"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "MumvZIqBNXHv",
        "outputId": "cabad4da-2d8d-4ad2-c975-9346520cc8bf"
      },
      "source": [
        "X"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>session_number</th>\n",
              "      <th>purchased</th>\n",
              "      <th>added_in_cart</th>\n",
              "      <th>checked_out</th>\n",
              "      <th>medium_used</th>\n",
              "      <th>device_used</th>\n",
              "      <th>os_used</th>\n",
              "      <th>day</th>\n",
              "      <th>year</th>\n",
              "      <th>weekname</th>\n",
              "      <th>is_weekend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>715</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2794</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3674</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5424</th>\n",
              "      <td>627</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5425</th>\n",
              "      <td>6710</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5426</th>\n",
              "      <td>198</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5427</th>\n",
              "      <td>913</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5428</th>\n",
              "      <td>1254</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5429 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      session_number  purchased  added_in_cart  ...  year  weekname  is_weekend\n",
              "0                715          1              0  ...     1         6           0\n",
              "1                 55          1              0  ...     1         4           0\n",
              "2                 11          0              0  ...     0         4           0\n",
              "3               2794          0              1  ...     0         1           0\n",
              "4               3674          0              0  ...     0         5           0\n",
              "...              ...        ...            ...  ...   ...       ...         ...\n",
              "5424             627          0              0  ...     0         6           0\n",
              "5425            6710          0              0  ...     0         4           0\n",
              "5426             198          0              0  ...     0         5           0\n",
              "5427             913          0              0  ...     0         5           0\n",
              "5428            1254          0              0  ...     0         1           0\n",
              "\n",
              "[5429 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WgH-41sIujGp",
        "outputId": "89ab0061-f61a-4121-daed-73d0f2b0240d"
      },
      "source": [
        "from statsmodels.stats.outliers_influence import variance_inflation_factor \r\n",
        "\r\n",
        "  \r\n",
        "# VIF dataframe \r\n",
        "vif_data = pd.DataFrame() \r\n",
        "vif_data[\"feature\"] = X.columns \r\n",
        "  \r\n",
        "# calculating VIF for each feature \r\n",
        "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) \r\n",
        "                          for i in range(len(X.columns))] \r\n",
        "  \r\n",
        "print(vif_data)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "           feature       VIF\n",
            "0   session_number  1.732430\n",
            "1        purchased  1.260914\n",
            "2    added_in_cart  1.242961\n",
            "3      checked_out  1.100704\n",
            "4      medium_used  1.866423\n",
            "5      device_used  2.890411\n",
            "6          os_used  4.236954\n",
            "7              day  3.251934\n",
            "8             year  1.330835\n",
            "9         weekname  2.848663\n",
            "10      is_weekend  1.371035\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y9IErrWtWC2x"
      },
      "source": [
        "# SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OCKWs-KOkWP"
      },
      "source": [
        "Y_to_check = train_data['time_spent']"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GXQY0e0lBx9P"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\r\n",
        "sc_X = StandardScaler()\r\n",
        "sc_y = StandardScaler()\r\n",
        "X = sc_X.fit_transform(X)\r\n",
        "Y = sc_y.fit_transform(Y)\r\n",
        "to_test = test_data.drop(['quarter','month'],axis=1)\r\n",
        "to_test = sc_X.fit_transform(to_test)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNMzHO7iyDWQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c45c7b7-4a9b-4351-8738-8dfb2e403656"
      },
      "source": [
        "from sklearn.svm import SVR\r\n",
        "#regressor = SVR(kernel = 'rbf',gamma=1e-8,epsilon=0.0104,tol=0.120)\r\n",
        "regressor = SVR(kernel = 'rbf',gamma=1e-8,epsilon=0.0101,tol=0.120)\r\n",
        "regressor.fit(X,Y)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.0101, gamma=1e-08,\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.12, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73sHcsVvDLUr"
      },
      "source": [
        "submission = regressor.predict(to_test)\r\n",
        "submission = sc_y.inverse_transform(submission)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpO8LeJsyNg7"
      },
      "source": [
        "submission = pd.DataFrame(data = submission,columns=['time_spent'])"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31Yk2Se4TyvY"
      },
      "source": [
        "# submission['time_spent'] = submission['time_spent'].abs()"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Tc3vwOY9TGfa",
        "outputId": "444ffd0f-288e-49c0-b04d-6a765e1e2037"
      },
      "source": [
        "submission.to_csv('submission.csv', index=False)\r\n",
        "from google.colab import files\r\n",
        "files.download('submission.csv')"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_64a6627e-9f2a-40d6-8b57-fb98847abc32\", \"submission.csv\", 43488)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95ClPXX3q-ky"
      },
      "source": [
        ""
      ],
      "execution_count": 71,
      "outputs": []
    }
  ]
}